Advertisements continue to be used to financially support provision of the content. However, output of advertisements is still traditionally linked to the content that is output with the advertisements and not to other factors that might exist “outside” of the content.
For example, advertisements for sporting goods might be included with a sporting event to increase a likelihood that the advertisement will reach a target consumer. However, in traditional techniques the advertiser has little to no information about the actual audience. Instead, the advertiser is forced to rely on general data collection techniques (e.g., polling techniques) to determine generalized characteristics of an audience that has watched the content or similar content in the past.
Thus, traditional advertising techniques leave the advertiser to make a “best guess” as to an audience that will interact with particular content, e.g., watch a television program, view a webpage, and so on. These traditional techniques also leave the advertiser to guess whether that audience has an increased likelihood of being a potential consumer for a product or service offered by the advertiser. Accordingly, traditional techniques may be inefficient as advertisers may have increased difficulty in reaching a target audience, consumers may be less likely to receive relevant advertisements, and content providers may lose revenue due to uncertainty of the audience.